Data-Driven Update of Table-Typed Controller Gain in Tracing Control System by Virtual Internal Model Tuning

In this paper, we present a new approach to data-driven update of table-typed controller gain in tracing control system. This type of control system is used in various precision processing systems for industrial products. It is important for a tracing control system to be maintained so as to achieve the accuracy of positioning for reducing the gap between the workpiece and the machining tool during the operation. In addition, it is also important to update the elements of the table in the controller as faciley from the practical points of view. The update of the controller by directly using the data without mathemaical models is one of the rational ways to realize them. From these backgrounds, we have previously applied FRIT (Fictitious Reference Iterative Tuning), which is a data-driven controller tuning method with only one-shot experiment, to update the table-typed controller in the tracing control system. However, this method requires calculations using a fictitious inverse system of an implemented controller, which leads to computational errors. In this paper, we propose a method utlizing VIMT (Virtual Internal Model Tuning) to update the table-typed controller without using its inverse system. Moerover, we update not only each gain in the table but also each corresponding range to improve the tracking accuracy of tracing control system.